Indoor localization by denoising autoencoders and semi-supervised learning in 3D simulated environment

Abstract

Robotic mapping and localization methods are mostly dominated by using a combination of spatial alignment of sensory inputs, loop closure detection, and a global fine-tuning step. This requires either expensive depth sensing systems, or fast computational hardware at run-time to produce a 2D or 3D map of the environment. In a similar context, deep neural networks are used extensively in scene recognition applications, but are not yet applied to localization and mapping problems. In this paper, we adopt a novel approach by using denoising autoencoders and image information for tackling robot localization problems. We use semi-supervised learning with location values that are provided by traditional mapping methods. After training, our method requires much less run-time computations, and therefore can perform real-time localization on normal processing units. We compare the effects of different feature vectors such as plain images, the scale invariant feature transform and histograms of oriented gradients on the localization precision. The best system can localize with an average positional error of ten centimeters and an angular error of four degrees in 3D simulation.